speculative

Coordinate parallel implementations and select the best solution via weighted criteria.

1|Updated Jan 13, 2026
One-click install
npx skills add https://github.com/wgordon17/personal-claude-marketplace --skill speculative
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: speculative
Source: https://github.com/wgordon17/personal-claude-marketplace/tree/main/code-quality/skills/speculative
Command: npx skills add https://github.com/wgordon17/personal-claude-marketplace --skill speculative

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Coordinating multiple viable approaches to complex problems by running parallel implementations, evaluating them with a formal judge, and selecting the best solution.

Core Features & Use Cases

  • Orchestrates 2-4 competing implementations in isolated workspaces with a unified evaluation framework.
  • Uses a weighted success-criteria model to rank results and determine the winner.
  • Produces auditable artifacts: implementation results, judge judgments, and a final speculative report.

Quick Start

Define the task and success criteria, spawn competing agents in isolated worktrees, and let the judge pick the winner.

Frequently Asked Questions about speculative

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run parallel implementations to choose the best solution for a problem?

Parallel implementations are orchestrated by spawning 2-4 competing agents in isolated workspaces, evaluating their results with a weighted success-criteria model, and selecting the highest-ranked solution.

What is the best way to evaluate competing agents and avoid guesswork?

Evaluating competing agents without guesswork requires a formal judge that applies weighted success criteria to rank the results, ensuring the winning solution is selected based on formal evaluation rather than intuition.

How do I orchestrate multiple agents in isolated workspaces for evaluation?

Orchestrating multiple agents involves spawning 2-4 competing implementations in isolated worktrees, executing them in parallel, and capturing their output as auditable artifacts for a unified evaluation framework.

Can I define custom weighted success criteria for parallel agent orchestration?

Yes, you can define custom weighted success criteria by creating a SpeculativeSpec that establishes the evaluation framework used by the judge to rank competing implementations and determine the winner.

What artifacts are produced when running parallel competing implementations?

Running parallel competing implementations produces auditable artifacts including the individual implementation results, the judge's formal judgments based on weighted criteria, and a final speculative report summarizing the evaluation.

When should I use a speculative approach instead of a single agent implementation?

Use a speculative approach when multiple viable options exist for a complex problem and formal evaluation is needed to avoid guesswork, rather than relying on a single implementation path.